Method and system of optimizing a web page for search engines

ABSTRACT

An organic search ranking of a web page for a particular search query can be optimized by publishing a large number of short, subsidiary web documents associated with the web page. The web page and the associated web documents can be individually optimized in terms of one or more parameters based upon web analytical data compiled for the search query. The web page and associated web documents can also be jointly optimized by coordinating the content of the associated web documents to align with the primary web page, and further by providing a network of links between the associated web documents and the primary web page. To provide greater insight and to assess the overall efficacy of the web presence optimization effort, historical search rank data can be correlated with specific events and reported to a user.

CROSS-REFERENCE TO RELATED APPLICATIONS

This application is a continuation of Ser. No. 13/014,147 filed Jan. 26,2011 which claims the benefit of U.S. Provisional Patent Application No.61/310,374, filed Mar. 4, 2010. The entirety of U.S. Provisional PatentApplication No. 61/310,374 and U.S. patent application Ser. No.13/014,147 are hereby incorporated by reference.

FIELD

The described embodiments relate to a method and computer system foroptimizing a web page for search engines, and more particularly tooptimizing an organic search ranking of the web page.

BACKGROUND

Internet search engines algorithmically search for web documents, suchas web page, weblogs (“blogs”), social media posts, images, videos, andvarious other documents types that are uploaded to the Internet.Typically, a user enters a search query, which could be a particularword or phrase, and in response the search engine will generate andpresent a listing of web documents to the user, which have beenidentified by the search engine as bearing some connection or relevanceto the entered search query. The various search results are ranked bythe search engine according to relevance, and then displayed in a listin order of decreasing relevance. The most relevant documents aresituated at the top of the search results. Less relevant documentsfollow in order. In some cases, a position at the top of the searchresults for a particular search query may be guaranteed by, essentially,purchasing that privilege from the proprietor of the search engine(referred to as a “sponsored search result”). Otherwise the searchranking of a given web page for a particular search query is determinedaccording to the search algorithm executed by the search engine.

Internet search engines typically do not search the Internet directly.Instead the search engine will “crawl” the Internet ahead of time andindex every web page it encounters according to content. To perform theindexing, the search engine extracts and analyzes content taken fromdifferent locations within the web page, for example keywords includedin the titles, headings, and meta tags of the web page. The extractedweb page data is stored (referred to as “caching”) in the index databasefor later search queries in the search engine. Some search engines cacheall or part of the source web page, as well as well as other potentiallyrelevant information about the web page. Other search engines cacheevery word of every web page that is crawled. Accordingly, when the userenters the search query into the search engine, typically in the form ofa keyword or keyword phase (though other more advanced search delimiterscan be made available as well), the search engine will access the indexdatabase directly to generate a list of best-matching web pages for theparticular search query.

How the search engine ranks web pages for a given search query is basedon a proprietary algorithm executed in the search engine to quantifyrelevance. Given the sheer number of different web pages on the Internetthat may include the same particular word or phase, some web pages arebound to be more relevant, popular, useful, authoritative, etc., thanother web pages. The search rank algorithm makes a reasoneddetermination of that relevance. In a sense, therefore, theeffectiveness of the search engine depends on its ability to parsethrough the volume to generate relevant search results for the user.Different search engines can apply different ranking algorithms basedupon different parameters and weighting factors, but almost all searchengines withhold specific details about their proprietary rankingalgorithm to safeguard its efficacy. Should the details of thealgorithms become widely known, web pages could then be cultivated tomaximize their rankings in the various ranking algorithms for aparticular search query without regard to the actual relevance of theweb pages. Most search engines also, for this reason, constantly updateand make changes to their ranking algorithms. The algorithms also evolveover time in response to changes in Internet usage and other externalfactors as new web techniques emerge.

SUMMARY

According to one aspect, some embodiments of the invention provide amethod of optimizing a web page for search engines. The method includesthe steps of: compiling web analytical data; determining, using aprocessor, a recommended modification to at least one parameter of a webdocument associated with the web page, the recommended modification toimprove an organic search ranking of the web page for a search queryperformed in one or more of the search engines, the determination madebased upon the web analytical data; applying, in a user interface, therecommended modification to the at least one parameter of the associatedweb document; generating at least one data graph of historical searchrank data including the organic search ranking of the web page for thesearch query over time; correlating, using the processor, event datawith a feature or trend in the at least one data graph; and displaying,in the user interface, the at least one data graph annotated with theevent data correlated with the feature or trend.

The associated web document can be each of a press release, a blog post,and a social media post.

The at least one parameter of the associated web document for which amodification is recommended can be each of title content, title length,keyword content, keyword location, document code structure, documentformat, document content, document headings, document tags, documentmeta content, blocker elements, web document indicator (ie URL), linkingstructures, and tracking codes.

The recommended modification can be to include at least one keyword orat least one keyword phrase in the associated web document. In someembodiments, the recommended modification can be to include the at leastone keyword in the title of the associated web document, one or moredata tags of the associated web document, one or more headings includedin the associated web document, or one or more linking structures in theassociated web document. The recommended modification can be to includethe at least one selected keyword in multiple locations throughout theassociated web document. The recommended modification can also be toinclude the at least one selected keyword in a specific position withinthe associated web document.

The recommended modification can be to restrict the title length of theassociated web document to a specified character limit.

The recommended modification can be to incorporate at least one linkingstructure in the associated web document. In some embodiments, the atleast one linking structure can provide a link from the associated webdocument to the web page, another location in the associated webdocument, other web documents associated with the web page, or other webdocuments having content similar to the web page.

The recommended modification can be to change the code structure of theassociated web document or to remove one or more blocker elements fromthe associated web document.

The recommended modification can be to include or alter one or more datatags or document headings in the associated web document.

The recommended modification can be to generate a short URL for theassociated web document.

The web analytical data can be distribution data for the associated webdocument, including each of a number or frequency of references to theassociated web document in other web documents, a number or frequency ofre-postings of the associated web document in other web documents, anumber or frequency of click-throughs to the associated web documentfrom other web documents, a number or frequency of backlink referrals, anumber or frequency of social bookmarks of the associated web document,a number or frequency of social sharing mentions of the associated webdocument, and a number or frequency of sales conversions.

The web analytical data can be search rank data for the web page,including a present or historical search ranking for each of the searchquery of the web page, the associated web document, and other webdocuments associated with competitors of the web page.

The event data can include timing data for each of the appliedmodification to the at least one parameter of the associated webdocument, the publication of new web documents associated with the webpage, social bookmarking of the associated web document, and socialsharing mentions of the associated web document.

The event data can be timing data for an alteration to a respectivesearch rank algorithm used in one or more of the search engines.

In some embodiments, the method includes the further steps of:determining, using the processor, a recommended modification to at leastone parameter of the web page to improve the organic search ranking ofthe web page for the search query in one or more of the search engines;and applying, in the user interface, the recommended modification to theat least one parameter of the web page.

In some embodiments, the method includes the further steps of:determining, using the processor, a recommended release schedule forpublishing new web documents associated with the web page, thedetermination made based upon the web analytical data; and publishing anumber of the new web documents according to the recommended releaseschedule.

In some embodiments, the method includes the further steps of:determining, using the processor, a recommended distribution schedulefor distributing links to published web documents associated with theweb page to social media sites, the determination made based upon theweb analytical data; and distributing the links to the published webdocuments according to the recommended distribution schedule.

In some embodiments, the user interface is presented as a plug-in for aweb development application.

According to another aspect, some embodiments of the invention provide asystem for optimizing a web page for search engines. The system includesa storage medium storing executable instructions and a processor coupledto the storage medium. The processor is programmed by the storedinstructions to: compile web analytical data; determine a recommendedmodification to at least one parameter of a web document associated withthe web page, the recommended modification to improve an organic searchranking of the web page for a search query performed in one or more ofthe search engines, the determination made based upon the web analyticaldata; prompt, in a user interface, to apply the recommended modificationto the at least one parameter of the associated web document; generateat least one data graph of historical search rank data including theorganic search ranking of the web page for the search query over time;correlate event data with a feature or trend in the at least one datagraph; and display, in the user interface, the at least one data graphannotated with the event data correlated with the feature or trend.

According to yet another aspect, some embodiments of the inventionprovide a non-transitory, computer-readable storage medium storinginstructions that are executable by a processor coupled to the storagemedium. The stored instructions program the processor to: compile webanalytical data; determine a recommended modification to at least oneparameter of a web document associated with a web page, the recommendedmodification to improve an organic search ranking of the web page for asearch query performed in one or more of the search engines, thedetermination made based upon the web analytical data; prompt, in a userinterface, to apply the recommended modification to the at least oneparameter of the associated web document; generate at least one datagraph of historical search rank data including the organic searchranking of the web page for the search query over time; correlate eventdata with a feature or trend in the at least one data graph; anddisplay, in the user interface, the at least one data graph annotatedwith the event data correlated with the feature or trend.

These and other features of the embodiments are set forth and describedherein.

BRIEF DESCRIPTION OF THE DRAWINGS

A detailed description of various embodiments of the invention,including a preferred embodiment, is provided herein below withreference to the following drawings, in which:

FIG. 1 is a schematic diagram illustrating the components of a computersystem configured for optimizing a web page for search engines,according to aspects of embodiments of the present invention.

FIG. 2 is a schematic diagram illustrating software modules that can beexecuted by the system illustrated in FIG. 1 to optimize a web page forsearch engines.

FIG. 3 is a flow chart illustrating the steps of a method performed bythe system illustrated in FIG. 1 for generating a web documentassociated with a primary web page to optimize an organic search rankingof the web page.

FIG. 4 is a flow chart illustrating the steps of a method performed bythe system illustrated in FIG. 1 for determining release anddistribution schedules for web documents associated with the web page.

FIG. 5 is a flow chart illustrating the steps of a method performed bythe system illustrated in FIG. 1 for generating an annotated graph ofhistorical search rank data for one or more different web documents.

It will be understood that the drawings are exemplary only and that anyreference to them is done for the purpose of illustration only, and isnot intended to limit the scope of the embodiments described hereinbelow in any way. For convenience, reference numerals may also berepeated (with or without an offset) throughout the figures to indicateanalogous components or features.

DESCRIPTION OF EXEMPLARY EMBODIMENTS

Search Engine Optimization (“SEO”) refers to the web science ofcustomizing web page content and format to increase the ranking of theweb page in search engines for particular search queries. Some SEOapproaches are considered to run contrary to Internet best practice andbe generally deceitful. For example, stuffing a web page with keywordsthat do not actually relate to the content of the web page and inlocations that do not naturally appear visible to the user is onefavoured approach. Key words can be included in document meta tags, indocument text that is for one reason or another invisible to the user,and elsewhere in the architecture of the web document that is not easilyviewable to the user. Another favoured approach is to generate numerousincoming hyperlinks to the web page from other web documents that havelittle to no value. In a worst case, perhaps, these other web documentsare nothing but “link farms” containing only a bare list of hyperlinksand existing almost exclusively just to elicit higher search rankings.

Increasingly there is a movement toward SEO initiatives that“organically” improve the search ranking of the web page. As compared tosome of the favoured tactics described above, these other SEOinitiatives are designed not only to improve the search ranking of a webpage for a particular keyword or keyword phrase that is actuallyrelevant to the web page, but to do so while maintaining the overallaesthetic quality of the web page and without substantially impairingits usability. In other words, the organic search ranking is achieved byembedding only keywords in the web page that bear a substantialrelevance to the content of the web page and that are, to at least someextent, naturally incorporated into the document text and not simplystuffed into the architecture of the document out of the sight of theuser. For organic search rankings, incoming hyperlinks to the webpagewill also originate from relevant and legitimate web pages and not fromlink farms. Similarly outgoing links should point to relevant andlegitimate documents for an organic search ranking.

In order to generate search rankings, Internet search engines more andmore are also emphasizing the currency of web page content and thefrequency at which new content is distributed to other web pages, inaddition to more traditional factors such as keywords and incominghyperlinks. One consequence of this shift in emphasis is that smaller,more frequently updated and widely shared web pages, in particular pressreleases, blogs, social media profiles, etc., are beginning to exhibithigh (and sometimes higher) search rankings as compared to moretraditional web pages, such corporate enterprise pages. This shiftingtrend also suggests that, in order to achieve high search rankings formore traditional web pages, increasingly it is becoming possible to takeadvantage of the perceived relevance that the search engines are placingon the newer, less traditional forms of web pages.

The embodiments described herein relate generally to a method andcomputer system for optimizing a web page for search engines. Thedescribed embodiments optimize the web page in terms of the abovedescribed traditional parameters, such as keywords and hyperlinks.However, the relationship between the primary web page and other webdocuments associated with the web page, such as press releases, blogposts and social media posts, is also optimized to provide a highersearch ranking for the primary web page than would otherwise have beenachieved through classic SEO initiatives alone. Accordingly, in aprocess of Web Presence Optimization (“WPO”), the wider presence of theweb page within a network of interrelated and connected web documents isemphasized, and not just certain facets of the web page itself, toimprove its performance in search engines.

According to Web Presence Optimization, a primary web page, which can bean enterprise web page, is associated with a number of frequentlypublished subsidiary web documents, such as press releases, blog posts,social media posts, and the like. The primary web page and thesubsidiary web documents are then interconnected using a networked setof cross-links between the subsidiary web documents and back-links tothe primary web page, to create a sort of center of gravity around theprimary web page. Additional outgoing links from the primary web page toother external web documents can also be provided. Moreover, the contentof each subsidiary web document is coordinated with that of the primaryweb page by embedding the same keyword or keyword phrases in strategiclocations throughout each associated document. Links to the associatedweb documents are also added to social media sites in order for the webdocuments to be widely distributed and shared between different users.Tracking historical search rank data relating to one or more differentweb documents over time, and correlating trends or features of resultingdata graphs with specific web presence optimization initiatives or otherexternal events also provides feedback and heightened insight into theoverall efficacy of the web presence optimization effort.

Referring now to FIG. 1, there is illustrated a schematic representationof a computer system 10, in accordance with embodiments of the presentinvention, which can be used for optimizing a web page for searchengines. The computer system 10 includes processor 20 coupled to storagedevice 25 and program memory 30. The processor 20 can be a dedicatedprocessor, such as a central processing unit (CPU), a graphicsprocessing unit (GPU), a Digital Signal Processor (DSP), a FieldProgrammable Gate Array (FPGA), an Application Specific IntegratedCircuit (ASIC), or the like. Alternatively, the processor 20 can be anyother processor included in a general purpose computer or workstation.The processor 20 can generally be any type of processor, which isconfigurable by an instruction set or sets stored in the storage device25 to execute software programs in the program memory 30.

Storage device 25 is coupled to the data processor 20 and comprises asuitable non-transitory, computer-readable storage medium. For example,the storage device 25 can include both volatile and non-volatile storagemedia, including but not limited to, random access memory (RAM), dynamicrandom access memory (DRAM), static random access memory (SRAM),read-only memory (ROM), programmable read-only memory (PROM), erasableprogrammable read-only memory (EPROM), electrically erasableprogrammable read-only memory (EEPROM), flash memory, magnetic media,optical media, and the like. The storage device 25 can be provided as anexternal storage device or database, such as a hard drive, compact disc(“CD”), digital video disc (“DVD”), memory card, memory stick, floppydisc, universal serial bus (“USB”) memory device, or any other deviceconfigured to store data. Alternatively, the storage device 25 can beprovided internal to the processor 20 or to the general purpose computeror workstation in which processor 20 is situated. One or more storagedevices, such as storage device 25, may be coupled to the processor 20.

Instructions stored in the storage device 25 are executed by theprocessor and, upon execution, program the processor 20 to execute oneor more software modules, as described herein, in the program memory 30coupled to the processor 20. In some embodiments, the program memory 30can comprise cache memory or other random access memory for dedicateduse by the processor 20 to execute the software modules. The function ofthe software modules includes, but is not limited to, performing thesteps of various methods, which are fully described herein below inaccordance with aspects of embodiments of the invention, to optimize aweb page for search engines. Thus, the various functions, acts or tasksperformed by the processor 20 are independent of the nature and type ofthe program memory 20 and the storage device 25, as well as the natureand type of the instructions stored in the storage device 25, and thetype of processor 20 or protocols implemented thereby. The functions,acts or tasks can also be performed by software, hardware, integratedcircuits, firmware, micro-code and the like, operating alone or incombination. Multiprocessing, multitasking, parallel processingstrategies and the like are also possible. Suitable configuration of theprocessor 20, storage device 25 and program memory 30 is within theunderstanding of the skilled person.

As seen in FIG. 1, the processor 20 is further coupled to display 35 andinput device(s) 40. The display 35 can be any display unit configurableby the processor 20, such as a liquid crystal display (LCD), an organiclight emitting diode (OLED), a flat panel display, a solid statedisplay, a cathode ray tube (CRT), a projector, a printer, or the like.As will be described more fully below, the display 35 is used to displaya user interface for software modules executed by the processor 20.Input device(s) 40 may be any suitable devices for inputting commands orother data to the processor 20 to be used in the software modules. Forexample, the input device(s) 40 can include a number pad, a keyboard, acursor control device, such as a mouse, or a joystick, remote control,and the like. It is also possible for the display 35 to comprise a touchscreen display, and thereby function itself also as an input device forthe processor 20.

Communication subsystem 45 is also coupled to the processor 20 formaking a connection with the network 50. The communication subsystem 45may be integrally provided within the processor 20, but may alsoalternatively be implemented as a separate component coupled to theprocessor 20. Moreover, the communication subsystem 45 may beimplemented using software, hardware or a combination thereof indifferent embodiments of the present invention. It will further beappreciated that network 50 may be any type of computer accessiblenetwork, including a personal area network (PAN), local area network(LAN), or wide area network (WAN), and may include wired networks,wireless networks, or combinations thereof. Moreover, the network 50 canbe a public network, such as the Internet, as well as a private intranetor combinations thereof. Communication subsystem 45 can make both awireless connection to the network 50, as well as a physical connection,such as a wired Ethernet connection, utilizing a suitable networkingprotocol such as TCP/IP, HTTP, FTP based networking protocols, and thelike. Accordingly, the communication subsystem 45 allows the processor20 to interface with other network components connected to the network50, such as databases, data servers, web servers, and othernetwork-connectable devices. The processor 20 is thereby capable ofcompiling data from network components it makes an interface with.

Referring now to FIG. 2, there is illustrated a schematic diagram of asoftware program that can be executed by the system 10 for optimizing aweb page for search engines. The software program is executed in programmemory 30 by the data processor 20 processing instructions stored in thestorage device 25. The executed software program can be provided as astandalone application within the system 10, but in some embodiments canalso be provided as a plugin utility for an external softwareapplication being run in the system 10. Thus, the software program canbe adapted using techniques that will be well understood forinstallation into the external application to be used therewith. Theexternal application to which the plugin utility interfaces can be a webdevelopment application for designing, creating and publishing web pagesor other web documents to the Internet. When used in conjunction withsuch a web development application, the plugin utility makes itpossible, for example, to optimize web pages and other web documents forsearch engines as they are being developed in the application using theplugin utility.

In a broad sense, the organic search ranking of a web page for aparticular search query can be improved by creating a large number ofadditional web documents around and pointing back to the primary webpage, which have similar content to the primary web page and which arefrequently updated as well as widely distributed. Each of the primaryweb page and the associated web documents can be individually optimizedfor the search query in terms of one or more parameters based upon webanalytical data compiled for the search query. However, the web page andassociated web documents can also be jointly optimized to improve theorganic search ranking of the primary web page by coordinating thecontent of the associated web documents to align with the primary webpage, and further by providing a dense network of links between theassociated web documents and the primary web page. Wide distribution ofthe associated web documents can be achieved by publishing them tosyndication entities and social media sites in particular. To providegreater insight and to assess the overall efficacy of the web presenceoptimization effort, historical search rank data can be correlated withspecific events and reported to the user. The interconnection ofsoftware modules illustrated in FIG. 2 can be used to provide theseresults.

As seen in FIG. 2, each of a data compiler 60, document optimizer 65,content scheduler 70, report generator 75, and user interface 80 may beexecuted in the program memory 30. The data compiler 60 can operate inconjunction with the communication subsystem 45 to compile webanalytical data from multiple different sources within the network 50.For example, as described more fully below, data compiler 60 can compileInternet search traffic data from one or more search engines or thirdpart web analytical systems that generates search traffic or search rankdata. Accordingly, the data collected by the data compiler 60 caninclude traffic levels from different search engines to different webpages for potentially different search queries. The search data mayfurther include data relating to how different search engines index webpages and, in particular, whether or not particular keywords have beenindexed. The data compiler 60 may also compile other data associatedwith web pages that have been indexed, such as its title, description,incoming and outgoing links, and URL. Optionally, the data compiler 60may compile data pertaining to which keywords are included in the title,document tags and/or headers of a web page. As should be appreciated,the data compiler 60 is configurable to located and gather searchtraffic data that would provide an indication of the amount of searchvolume that different keywords or keyword phrases are generating.Generally it is desirable for web pages to have high search ranks forpopular search queries.

In some embodiments, the data compiler can further compile differentpage-level data for a web page, for example including URL length, pagesize, keyword density, inclusion of animation interfaces or otherblocker elements, inclusion of header tags, and the like. It will beappreciated that the type and quantity of data that may be compiled bythe data compiler 60 can be unlimited.

By interfacing with the network 50 the data compiler 60 may furthercompile historical search rank data for a web page in respect of aparticular search query. In other words, the data compiler 60 can trackthe ranking of the web page for the particular search query over time.Search rank data can be compiled not just for the primary web page, butalso any web document associated with the primary web page andadditional web documents not directly associated with the primary webpage. One class of web documents that it may be desirable to trackconsists of other web documents having similar content to the primaryweb page, such as competitor web pages.

The data compiler 60 can also compile distribution data for a web pageor web document, which in effect tracks how widely a document has beendistributed on social media sites. This can include, for example, ameasure of how many times or how frequently one of the web documentsassociated with the primary web page has been referenced in another webdocument on the social media site. Similarly, the distribution data caninclude a measure of how many times or how frequently one of the webassociated documents has been re-posted to the social media site, orclicked-through from another web document on the social media site, orbeen included in a backlink referral or social bookmarking in the socialmedia site, or been mentioned in the social media site. All of thesedifferent types of distribution data can be compiled by the datacompiler 60 to provide an indication of a web document's distribution inthe social media environment.

Document optimizer 65 is linked to the data compiled 60 and the userinterface 80, and can process a web document to determine aspects orparameters of a web page or associated web document to modify forimproving the organic search ranking of the web page. At a first level,the document optimizer 65 can perform an audit of the web page orassociated web document to determine page level parameters to change.For example, the document optimizer 65 can examine document format andcode structure, use of tags and headers, URL, document size, and thelike to determine recommended modifications. At a second level, however,the document optimizer 65 can analyze the use of keywords andhyper-links in the web page or associated web document, in comparison tothe web analytical data compiled by the data compiler 60, to determinerecommended modifications to these document parameters as well.

Moreover, given the increasing emphasis that Internet search engines areplacing on the frequency of content updates to determine searchrankings, the document optimizer 65 can also optimize the relationshipof a primary web page to other web documents associated with it. Thus,the document optimizer 65 can identify aspects or parameters of twoassociated web documents that do not match each other and then makerecommendations to modify one of the documents to match the other. As anexample, if two web documents have generally similar content butexpressed using different keywords, it can be advantageous to modify oneof the documents to use keywords that are consistent with the other.That way, the associated web document will have a higher perceivedrelevance in Internet search engines, thereby achieving higher searchrankings as well. For any of the above cases, the document optimizer islinked to the user interface 80, in which the user can be prompted tomake a recommended change to the target web document. Upon beingprompted, the user can apply the recommended modification to the atleast one parameter of the associated web document.

Content scheduler 70 is also functionally linked to the data compiler 60and the user interface 80. Web analytical data compiled by the datacompiler 60, such as search rank and distribution data is received bythe content scheduler 70. Based upon the received data, the contentscheduler 70 can then determine a recommended distribution schedule fordistributing different web documents associated with the primary webpage throughout various social media sites. The distribution schedulecan be determined depending on many different factors to maintain theperceived relevance and currency of a web document, and for example canbe determined relative to a corresponding distribution schedule foranother high ranking web page taken as a benchmark target. In a similarfashion, the content scheduler 70 can also determine a release schedulefor new web documents (again e.g. blog posts and press releases)associated with the primary web page, with the determination again beingmade based upon the web analytical data compiled by the data compiler60. By releasing new web documents and then distributing links to theseweb documents to social media sites, with sufficient regularity, freshcontent will continuously circulate and point to the relevance of theprimary web page. The organic search ranking of the primary web page canbe improved as a result.

Report generator 75 can be configured to process web analytical datatogether with logged event data in order to determine correlationsbetween the two data sets. The web analytical data can includehistorical search rank data (i.e. ranking over time for a given searchquery) for a web page. In other words, the search ranking of the webpage will be tracked for one or more selected search queries over time.Event data will also be logged in the data compiler 60 for events thatmay have had an impact (positive or adverse) on search ranking. Theevent data can refer to internal events that occur as a direct result ofa WPO initiative, i.e. making recommended modifications to the primaryweb page or associated web documents, releasing and distributing newassociated web documents, and the like, as these events could allpotentially impact the search ranking of the primary web page. However,the event data logged in the data compiler 60 is not limited just tointernal data and can include external event data as well. One exampleof an external event that could be logged is a change to a competitorweb page that had a positive affect on its search ranking for a searchquery that the primary web site would desirably have a high ranking foras well. Another possibility is a change in the respective rankingalgorithms used in the search engines to rank search results, as theseevents also could potentially impact the search rank of the primary webpage.

The report generator 75 is configured to generate at least one datagraph of historical search rank data for the primary web page, and thendetermine a causal correlation between features or trends in the datagraph and events in the event log, in the sense that a particular eventor sequences of events is determined to have caused in some part theobserved trend or feature in the graph. To accomplish this task, theevent log can include timing data for each of the logged events. It canthen be determined whether an event that occurred in the timeframe of anobserved feature or trend of the graph could explain the feature ortrend. For example, possible features of the data graph that might becorrelated with specific events could include local minima or maxima(indicating a reversal in the search rank trend), unusually large jumpsor drops in search rank, steady increases or decreases in search rank,and so forth. Events occurring in the timeframe of the observed featureor trend can be put forth as having bearing a causal relation to thetrend. Data graphs generated by the report generator 75 can be displayedin the user interface 80. Of course, it should be appreciated that thereport generator 80 need not be limited to generating data graphs, andcan also generate other data forms, such as tables and charts, whichequivalently bring attention to possible correlations between trends inhistorical search rank for display in the user interface 80.

Referring now to FIG. 3, there is provided a flow chart illustrating thesteps of a method 100, in accordance with aspects of embodiments of thepresent invention, for generating a web document associated with a webpage in order to optimize the web page for search engines. The method100 can be performed, for example, by the processor 20 in the programmemory 30 in conjunction with other system components where appropriate.However, it should be understood that the specific steps illustrated inFIG. 3 are exemplary only and need not necessarily be performed in theorder shown. Other additional steps herein described but notspecifically shown may be included in the method 100, just as some ofthe steps specifically shown may also be omitted without departing fromthe scope of the present disclosure. The method 100 may also, of course,be performed repeatedly to generate multiple web documents associatedwith the primary web page, as described herein.

At step 105, a web document associated with the primary web page isgenerated. The associated web document can be a blog post containing,for example, editorial content that is related to the subject matter ofthe primary web page. Similarly, the associated web document can also bea press release containing a news item or other current information thatmay relate somehow to the primary web page. The blog post or pressrelease can be published as a separate web document distinct from theprimary web page. But in some embodiments, the blog post or pressrelease can be published in a section or sub-page of the primary webpage. Of course, this listing of the different possible types of webdocuments is not intended to be limiting. The associated web documentmay in other instances also be a posting to a social media site or thelike.

At step 110, at least one linking structure, which could be a hyperlink,is provided between the primary web page and the associated webdocument. As will be appreciated, the linking structure can be providedin the form of a hyperlink embedded into the text or graphics of theassociated web document. The hyper-link may be a cross-link to other webdocuments associated with the primary web page, but may also be aback-link from the associated web document to the primary web page. Ifdesired, and in some embodiments advantageously, different back-linksand cross-links from different associated web documents can point todifferent sections or locations within the target documents based uponthe immediate context of the embedded hyper-link. By thus providingcross-links and back-links to other associated web documents and theprimary web page, respectively, a dense network of inter-linked webdocuments will evolve and be centered on the primary web page. Thisnetwork of cross-links and back-links to and from other web documentscan positively influence the search ranking of the primary web page onwhich the associated web documents are centered.

At step 115, a short URL is generated and then assigned to theassociated web document. Some web documents when they are first createdare assigned extremely long and unintuitive URLs, often consisting oflong random or pseudo-random character sequences. Aside from the factthat these URLs may be practically impossible to remember and justgenerally difficult to work with overall, their extreme length isactually incompatible for certain uses and applications. For example,some search engines will perform keyword analysis on an initialcharacter block of the URL only, i.e. only the first characters up to amaximum limit. Thus, if the URL of a web document is automaticallygenerated to include random character strings or to contain more thanthe maximum allowed number of characters, then it may not be optimal forsearch engines. By creating a short URL for the associated web document,however, keywords can be included within the first part of the URL andthereby be visible to the search engines.

As another example, some social media sites impose a hard characterlimit on the number of characters each posting to the social media sitemay contain. That limit can be as low as 140 characters, which some ofthe automatically generated URLs containing random character strings mayin fact approach or even exceed. By creating a short URL for the webdocuments associated with the web page, it is possible to restrict theshort URL to a character count that will be acceptable for posting tothe social media site. Thus, the newly created and optimized short URLcan then be distributed to other Internet users by posting to differentsocial media sites. This too can have a positive effect on the searchranking of the primary web page with which the additional web documentsare associated.

In step 120, web analytical data that will be used to optimize one ormore parameters of the associated web document is compiled. The compiledweb analytical data can include search analytical data, such as searchengine statistics, search volume trends and related analyses, reversesearching (e.g. entering web pages to identify their keywords), keywordmonitoring, search result and advertisement history, advertisementspending statistics, web page comparisons, affiliate marketingstatistics, multivariate ad testing, and the like. As will beunderstood, the search analytical data can be compiled in differentpossible ways. As one example, many search engines will in fact provideaccess to their own data using third party services, such as GoogleTrends and Google Insights, which in turn collect data from internetsource providers (ISP), scraping search engines, and various othermeans.

Collecting Internet traffic statistics from ISPs can provide for broaderreporting of web traffic as compared to simple keyword monitoring orreverse searching. For example, the compiled search analytical data canbe processed in order to discern overall Internet search trends likewhich keywords Internet users tend to be including in their searchqueries for a given topic. Other search trends than can be extractedfrom the compiled data can include such things as frequency of keyworduse, click-through rates for given key words, average time spent at atarget web page in a list of search results, and the like.

In step 125, the associated web document is crawled in order to indexits content. In other words, the associated web document is analyzed inorder to determine the general nature of its content as represented byone or more identified key words. In crawling the associated webdocument, locations in which identifiable key words appear can also benoted, as well as locations in which no identifiable key words appear.Then in step 130, the indexed content is analyzed based on the compiledweb analytical data in order to determine parameters or aspects of theassociated web document that could be modified to improve the searchranking of the primary web page. As one example, the keywords identifiedin the indexed content can be compared with the search analytical dataas a basis for recommending changes to the content and/or structure ofthe associated web document.

In step 135, based upon the comparison of the indexed content with thesearch analytical data, a user can be prompted to make a recommendedmodification to one or more parameters of the associated web document inorder to improve the organic search ranking of the primary web page. Forexample, the recommended modification can be to include one or morekeywords or keyword phrases in the associated web document. The keywordsor keyword phrases to include can be determined based upon the webanalytical data and the indexed content of the associated web document,as well as indexed content from the primary web page with which the webdocument is associated, such that more effective use of keywords ismade. In particular, the recommendation can be to substitute popular andfrequently used keywords or keyword phrases that are relevant to thecontent of the associated web document for corresponding less popularkeywords that are currently being used. This way the web documents willbe optimized for keywords that are actually being searched. Of course,the popularity of keywords can also change over time, as reflected inthe web analytical data, and thus the recommendation can also be tochange one or more existing keywords.

Certain locations throughout web documents, as it will be appreciated,are given more emphasis than other locations for inclusion of keywordsor keyword phrases. Thus, the recommended modification can be to includea selected keyword or keyword phrase in a specific location in theassociated web document, such as the document title or one or moresection headings within the document. The recommended modification canalso be to include the selected keyword or keyword phrase in one or moredata tags included in the structure or architecture of the web document.This can be the case where the web document is published in HyperTextMarkup Language (HTML) or Extensible Markup Language (XML), for example.The recommended modification can also be to include the selected keywordor keyword phrase in one or more of the linking structures included inthe associated web document. This can be the case where selected keywordrelates to a specific topic or item and the link is directed to anotherweb document providing more detailed information on the topic. Thus, therecommended modification can also be to include the selected keyword orkeyword phrase in multiple different locations through the associatedweb document.

The recommended modification can also relate to parameters of theassociated web document other than keywords. For example, search enginesin some instances will also only index a maximum number of initialcharacters in a document title, which implies that only keywords locatedwithin the maximum character limit will be discovered by the searchengines. Thus, the recommended modification can be to restrict thelength of the document title to fall within the maximum character limit,which can be about 75 characters or so. In the same way, the recommendedmodification can be to include at least one keyword or keyword phrasewithin the first 75 characters of the document title. In a similar way,the recommend modification can be to include or alter document headingswithin the body of the web document, and also to include or alter one ormore data tags within the code structure of the web document.

Moreover, some web document formats are more search-engine friendly, andthus more susceptible to indexing by the search engine, than others.Animation interfaces, like Adobe Flash, are one well known example of aweb document format that is not indexed very efficiently by Internetsearch engines. Thus, the recommended modification can be to remove anyanimation interfaces that may have been included in the associated webdocument, and to regenerate the web document using an alternativedocument format, such as a suitable markup language that will increasethe visibility of the web document to the search engines. Othermodifications to the document code structure and removal of blockerelements (i.e. aspects of the web document format that block indexing bythe search engines) can be recommended as well.

As described above, the associated web document is generated includingone or more linking structures to the web page or to other web documentsassociated with the web page. Nonetheless, based upon the compiled webanalytical data, the recommended modification can be to include one ormore additional linking structures, for example where it is determinedthat additional linking structures are required to bring the web pageinto line with average Internet trends. Thus, the recommended at leastone additional linking structure can point to the primary web page, orto other of the web documents associated with the web page. However, insome embodiments, the linking structure can point to another locationwithin the associated web document. The linking structure can also evenpoint to other web documents having similar content to the primary webpage or to a relevant reference article. The recommended modificationcan also be to include keywords or keyword phrases in the linkingstructure.

It should be appreciated that certain steps 120 to 135 are, withsuitable modification, applicable to the primary web page directly inorder to improve its organic search ranking. For example, by compilingweb analytical data in step 120 and crawling the primary web page toindex content in step 125, a comparison can be made between the indexcontent and the web analytical data in step 130 in order to identify oneor more parameters of the primary web page for recommended modification.Essentially the same set of parameters can be identified in the primaryweb page as can be identified in the associated web documents and willtherefore not be listed again here for clarity and brevity.

Referring now to FIG. 4, there is provided a flow chart illustrating thesteps of a method 200 for determining for determining release anddistribution schedules for web documents associated with a primary webpage. The method commences at step 205, which comprises compiling webanalytical data, including distribution data for one or more webdocuments associated with a primary web page. As step 205 issubstantially the same as step 120 in method 100, it will not bediscussed again in any great detail.

In step 210, a recommended release schedule for new web documentsassociated with the primary web page is determined based upon the webanalytical data. The recommended release schedule can comprise both arecommended number of documents to release and a recommended releasefrequency for the documents. The recommended number and frequency can bedependent on average trends determined after processing of the compileddistribution data. In other words, in order to have a positive impact onthe search ranking of the primary web page, it can be determined torelease new associated web documents at a frequency at least equal to anaverage release frequency or, more generally, having any selectedrelation to the average release frequency. Alternatively, therecommended release frequency can be determined with relation to therelease frequency of another, separate high ranking web page, taken as aform of benchmark number to match or, in some cases, exceed.

In step 215, similar to step 210, a recommended distribution schedulefor existing web documents associated with the primary web page isdetermined based upon the web analytical data. The recommendeddistribution schedule can include the recommended number and frequencyof times that the published web documents (e.g. blogs or press releases)will be distributed to social media sites. Distributing a web documentto a social media site, it is recalled, can involve generating a shortURL for the web document and then posting the short URL to the socialmedia site. Again the recommended number and frequency of distributionscan be determined with reference to a high ranking web page for the samesearch query and be determined to ensure also a high ranking of thetarget web page, or in the very least to improve the search ranking ofthe web page for that search term.

In step 220, the user can be prompted in a user interface to generatenew web documents associated with the primary web page, and subsequentlydistribute the web documents to social media sites, at the recommendedrelease and distribution schedules. These efforts, as will be describedbelow, can then be correlated with trends (either positive or negative)in the search ranking of the web page for the particular search query.

Referring now to FIG. 5, there is provided a flow chart illustrating thesteps of a method 300 for generating an annotated graph of historicalsearch rank data for a web page. The method 500 commences at step 305,which comprises compiling web analytical data, including historicalsearch rank data for one or more web documents associated with a primaryweb page. As step 305 is substantially the same as step 120 in method100 and step 205 in method 200, it will not be discussed again in anygreat detail.

In step 310, an event log is created. The event log can comprise a listof events together with certain event data, such as timing informationfor the event. In other words, the event log can include a tinningsequence for a list of identified events. The events can refer todifferent initiatives taken to improve the organic search ranking of theprimary web page, including but not limited to, modifications to aparameter of the web page or a web document associated with the webpage, release and distribution of new web documents associated with theweb page, and other global tasks performed to the web page or associatedweb documents. The event log can also include event data for additionalexternal events, such as the timing of changes made to the rankingalgorithms employed by the various search engines, changes to competitorweb sites, and the like. In other words, the event log can compile alist of all events that may have had an impact, whether positive oradverse, to the search ranking of the primary website or an associatedweb document.

In step 315, a graph of historical search rank data for one or more webdocuments is created. The graph can include trend curves for any or allof the primary web page, associated web documents, and othernon-associated web pages or documents, such as competitor web pages. Forexample, the trend curves can represent the search ranking for aparticular search query. It will be understood as well that thatmultiple trend curves could thus be created corresponding to differentsearch queries that the user may wish to track.

In step 320, trends or features in the graph of historical searchresults are identified and then correlated with events in the event log.For example, upward trends in the historical search rank of a website(indicating an improved search rank), which occur around the time ofmodifications to the primary web page or associated web documents, canbe correlated in the sense that the modification is identified as beinga possible cause for the upward trend. Similarly an upward trend in thegraph can be correlated with the release and distribution of a new pressrelease or blog associated with and linked back to the main web pagebeing tracked. At the same time, negative trends in the historicalsearch rank can also be correlated with, for example, the absence of newreleases. The negative trend could also be correlated with changes ormodifications to a competitor web page that results in a correspondingupward trend in that web page's search rank. External data, such aschanges to the ranking algorithms implemented in the search engines canalso be correlated, depending on the case, with either upward ordownward trends in the graph. Thus, the trends or features in the graphscan be correlated with certain identified event data and, morespecifically, the timing and projected impact of those events on searchrank.

In step 325, once trends or features of the graph have been correlatedwith specific events, the graph can be annotated to include thecorrelated event data. By including the annotations in the graph, moreinsight into how a web page's search rank can be affected will beobtained. The annotations can be included in the graph at or near thecorrelated trend or feature in order to reinforce the correlation, butthis need not be the case. It will be appreciated that many differentannotation strategies will be possible. The graph once annotated can bepresented in the user interface 80, for example.

The present invention has been described here by way of example only.Various modification and variations may be made to these exemplaryembodiments without departing from the spirit and scope of theinvention, which is limited only by the appended claims.

1. A method of optimizing a web page for a search engine, the methodcomprising: generating an event log comprising one or more eventsrepresenting one or more initiatives affecting an organic search rankingof the web page for a search query performed in the search engine;generating at least one data graph of historical search rank dataincluding the organic search ranking of the web page for the searchquery over time; identifying a feature or trend in the at least one datagraph; automatically correlating, using a processor, the event data withthe feature or trend identified in the at least one data graph toautomatically identify the one or more events causing the feature ortrend in the at least one data graph; determining, using the processor,a recommended modification to at least one parameter of a web documentassociated with the web page, the recommended modification to improvethe organic search ranking of the web page, the determination made basedon the web analytical correlation; and prompting in a user interface,the recommended modification.
 2. The method of claim 1, wherein theassociated web document comprises at least one of a press release, ablog post, and a social media post.
 3. The method of claim 1, whereinthe at least one parameter of the associated web document comprises anyone of: title content, title length, keyword content, keyword location,document code structure, document format, document content, documentheadings, document tags, document meta content, blocker elements, webdocument indicator (ie URL), linking structures, and tracking codes. 4.The method of claim 3, wherein the recommended modification comprises atleast one of: modifying the associated web document content to includeat least one keyword; modifying the associated web document content toinclude at least one keyword phase; restricting the title length of theassociated web document to a specified character limit; incorporating atleast one linking structure in the associated web document; generating ashort URL for the associated web document; changing the document codestructure or removing one or more blocker elements from the associatedweb document; including one or more data tags or document headings inthe associated web document; altering one or more data tags or documentheadings in the associated web document.
 5. (canceled)
 6. The method ofclaim 4, wherein the recommended modification comprises recommendingincluding the at least one keyword in one or more of the web documenttitle, one or more data tags of the associated web document, one or moreheadings included in the associated web document, and one or morelinking structures in the associated web document.
 7. The method ofclaim 6, wherein the recommended modification comprises including the atleast one selected keyword in multiple locations throughout theassociated web document.
 8. The method of claim 6, wherein therecommended modification comprises including the at least one selectedkeyword in a specific position within the associated web document. 9.(canceled)
 10. (canceled)
 11. The method of claim 4, wherein the atleast one linking structure provides a link from the associated webdocument to one or more of the web page, another location in theassociated web document, other web documents associated with the webpage, and other web documents having content similar to the web page.12. (canceled)
 13. (canceled)
 14. (canceled)
 15. (canceled)
 16. Themethod of claim 1, further comprising: compiling distribution data forthe associated web document; and determining the recommendedmodification based on the distribution data.
 17. The method of claim 16,wherein the distribution data relates to one or more of: a number orfrequency of references to the associated web document in other webdocuments, a number or frequency of re-postings of the associated webdocument in other web documents, a number or frequency of click-throughsto the associated web document from other web documents, a number orfrequency of backlink referrals, a number or frequency of socialbookmarks of the associated web document, a number or frequency ofsocial sharing mentions of the associated web document, and a number orfrequency of sales conversions.
 18. The method of claim 1, furthercomprising: compiling search rank data for the web page; and determiningthe recommended modification based on the search rank date.
 19. Themethod of claim 18, wherein the search rank data comprises a present orhistorical search ranking for the search query of at least one of theweb page, the associated web document, and other web documentsassociated with competitors of the web page.
 20. The method of claim 1,wherein the event data comprises timing data for at least one of amodification to the at least one parameter of the associated webdocument, timing data for publication of new web documents associatedwith the web page, timing data for social bookmarking of the associatedweb document, and timing data for social sharing mentions of theassociated web document.
 21. The method of claim 1, wherein the eventdata comprises timing data for an alteration to a respective search rankalgorithm used in one or more of the search engines.
 22. (canceled) 23.The method of claim 1, further comprising: determining, using theprocessor, a recommended release schedule for publishing new webdocuments associated with the web page, the determination made basedupon the correlation; and publishing a number of the new web documentsaccording to the recommended release schedule.
 24. The method of claim23, further comprising: determining, using the processor, a recommendeddistribution schedule for distributing links to published web documentsassociated with the web page to social media sites, the determinationmade based upon the correlation; and distributing the links to thepublished web documents according to the recommended distributionschedule.
 25. The method of claim 1, further comprising presenting theuser interface as a plug-in for a web development application.
 26. Asystem for optimizing a web page for a search engine, the systemcomprising: a storage medium storing executable instructions; aprocessor coupled to the storage medium, the processor programmed by theinstructions to: generate an event log comprising one or more eventsrepresenting one or more initiatives affecting an organic search rankingof the web page for a search query performed in the search engine;generate at least one data graph of historical search rank dataincluding the organic search ranking of the web page for the searchquery over time; identify a feature or trend in the at least one datagraph; automatically correlate the event data with the feature or trendidentified in the at least one data graph to automatically identify theone or more events causing the feature or trend in the at least one datagraph; determine a recommended modification to at least one parameter ofa web document associated with the web page, the recommendedmodification to improve the organic search ranking of the web page, thedetermination made based on the correlation; and prompt, in a userinterface, to apply the recommended modification to the at least oneparameter of the associated web document.
 27. A non-transitory,computer-readable storage medium storing instructions executable by aprocessor coupled to the storage medium, the instructions forprogramming the processor to: generate an event log comprising one ormore events representing one or more initiatives affecting an organicsearch ranking of a web page for a search query performed in a searchengine; generate at least one data graph of historical search rank dataincluding the organic search ranking of the web page for the searchquery over time; identifying a feature or trend in the at least one datagraph; automatically correlate the event data with the feature or trendidentified in the at least one data graph to automatically identify theone or more events causing the feature or trend in the at least one datagraph; determine a recommended modification to at least one parameter ofa web document associated with the web page, the recommendedmodification to based on the correlation; and prompt, in a userinterface, to apply the recommended modification to the at least oneparameter of the associated web document.
 28. The method of claim 1,further comprising applying the recommended modification to the at leastone parameter of the associated web document in the user interface. 29.The method of claim 1 wherein the at least one data graph of historicalsearch rank data comprises: a first data graph including the organicsearch ranking of the web page for the search query performed in thesearch engine over time; and a second data graph including a secondorganic search ranking of the web page for a search query performed in asecond search engine.
 30. A method of optimizing a web page for a searchengine, the method generating an event log comprising one or more eventsrepresenting one or more initiatives affecting an organic search rankingof the web page for a search query performed in the search engine;generating at least one data graph of historical search rank dataincluding the organic search ranking of the web page for the searchquery over time; identifying a feature or trend in the at least one datagraph; automatically correlating, using a processor, the event data withthe feature or trend identified in the at least one data graph toautomatically identify the one or more events causing the feature ortrend in the at least one data graph; trend identified in the at leastone data graph to automatically identify the one or more events causingthe feature or trend in the at least one data graph; determining, usingthe processor, a recommended modification to at least one parameter ofthe web page, the recommended modification to improve the organic searchranking of the web page, the determination made based on thecorrelation; and prompting, in a user interface, the recommendedmodification.